论文笔记之：Asynchronous Methods for Deep Reinforcement Learning
Asynchronous Methods for Deep Reinforcement Learning ICML 2016 深度强化学习最近被人发现貌似不太稳定，有人提出很多改善的方法，这些方法有很多共同的 idea：一个 online 的 agent 碰到的观察到的数据序列是非静态的，然后就是，online的 RL 更新是强烈相关的。通过将 agent 的数据存储在一个 expe
相关文章
- 1. 【5分钟 Paper】Asynchronous Methods for Deep Reinforcement Learning
- 2. 论文笔记：《Playing Atari with Deep Reinforcement Learning》
- 3. 论文阅读15：Massively Parallel Methods for Deep Reinforcement Learning
- 4. 论文笔记4：Language Understanding for Text-based Games using Deep Reinforcement Learning
- 5. 【论文笔记】Deep Reinforcement Learning for Page-wise Recommendations
- 6. Deep Reinforcement Learning for Dialogue Generation阅读笔记
- 7. Learning Deep Features for Discriminative Localization论文笔记
- 8. 论文笔记：Learning how to Active Learn: A Deep Reinforcement Learning Approach
- 9. Deep Reinforcement Learning
- 10. [Reinforcement Learning] Policy Gradient Methods
- 更多相关文章...
- • ASP.NET Razor - 标记 - ASP.NET 教程
- • Scala for循环 - Scala教程
- • Tomcat学习笔记（史上最全tomcat学习笔记）
- • 互联网组织的未来：剖析GitHub员工的任性之源
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 【5分钟 Paper】Asynchronous Methods for Deep Reinforcement Learning
- 2. 论文笔记：《Playing Atari with Deep Reinforcement Learning》
- 3. 论文阅读15：Massively Parallel Methods for Deep Reinforcement Learning
- 4. 论文笔记4：Language Understanding for Text-based Games using Deep Reinforcement Learning
- 5. 【论文笔记】Deep Reinforcement Learning for Page-wise Recommendations
- 6. Deep Reinforcement Learning for Dialogue Generation阅读笔记
- 7. Learning Deep Features for Discriminative Localization论文笔记
- 8. 论文笔记：Learning how to Active Learn: A Deep Reinforcement Learning Approach
- 9. Deep Reinforcement Learning
- 10. [Reinforcement Learning] Policy Gradient Methods